Data Science MSc

This course will equip you with the theoretical and algorithmic basis for understanding learning systems and the associated issues with very large datasets/data dimensionalities. You will be introduced to algorithmic approaches to learning from exemplar data and will learn the process of representing training data within appropriate feature spaces for the purposes of classification. You will also focus on basic data structures and algorithms for efficient data storage and manipulation. The major classifier types are taught before introducing the specific instances of classifiers along with appropriate training protocols. You will explore where classifiers have a relationship to statistical theory as well as notions of structural risk with respect to model fitting. You will be equipped with techniques for managing this in practical contexts.

This module provides an understanding of the methods, theories and techniques relevant to interactive visual data analysis. You will learn relevant principles and practices in visual data analysis design, implementation, and evaluation. You will gain experience in researching, designing, implementing, and evaluating your own visual analysis solutions, using both off-the-shelf tool-kits and data visualisation programming libraries. You will gain the knowledge to support your future employment or research in the fast-developing areas of data science, particularly visual analytics.

This course will provide an in-depth of the tools and systems used for mining massive dataset and, more in general, an introduction to the fascinating emerging field of Data Science. The module is divided in two parts: The first part focuses on the languages Python and R, a statistical learning language used to learn from data. This part provides an overview of the most common data mining and machine learning algorithms and every discussed concept is accompanied by illustrative examples written in Python and R languages. The second part of the module takes a tour through cloud computing and big data systems and teaches the participant how to effectively use them. Specifically, platforms and systems like OpenStack, Hadoop, MapReduce, MongoDB, Spark and NoSQL databases are introduced and every concept is accompanied by a number of illustrative examples.

This module focuses on legal, ethical and security requirements that underpin the technical processes and practice of data science (the collection, preparation, management, analysis and interpreting of large amounts of data called big data). Data science leads to predictive analyses and insights into big data for businesses, healthcare organisations, governments and security services among others. The volume of data collected, stored and processed brings many concerns especially related to privacy, data protection, liability, ownership and licensing of intellectual property rights and information security. This module will explore how data can be fairly and lawfully processed and protected by legal and technical means. It will give students a comprehensive understanding of important legal domains/regulatory issues, relevant ethical theories/guidance and important information security management policies that impact on the practice of data science. Further it will equip student with the necessary foundations to develop high professional standards when working as data scientists.

The project module aims to develop your knowledge and skills required for planning and executing research projects such as proof of concept projects or empirical studies related to data science. To plan and carry out your projects you will have to:

• Apply theories, methods and techniques previously learned.
• Critically analyse and evaluate research results drawing on knowledge from other modules.
• Develop your communication skills to enable you to communicate your findings competently in written and oral form.

How we'll teach you

You'll develop your knowledge and understanding of the subject through a combination of traditional lectures, small group discussions, small group and individual exercises, lab sessions and an individual research project.

Throughout your studies, we'll encourage you to undertake independent study both to supplement and consolidate what you're learning and to broaden your individual knowledge and understanding of the subject. Critical evaluation and selection of techniques and solutions will help you relate theory to practice.

You'll be taught by an experienced teaching team with a wide range of expertise and professional experience.

You will be based at our north London campus.

Whether you are studying full or part-time – your course timetable will balance your study commitments on campus with time for work, life commitments and independent study.

If you're studying full-time, you'll typically be expected to attend four modules per week, with each module consisting of three hours of weekly class time. If you're studying part-time, you'll generally be expected to attend two modules per week.

We aim to make timetables available to students at least 2 weeks before the start of term. Some weeks are different due to how we schedule classes and arrange on-campus sessions.

In a typical year, you’ll spend about 1200 hours on your course.

Outside of teaching hours, you’ll learn independently through reading articles and books, working on projects, undertaking research, and preparing for assessments including coursework and presentations.

Typical weekly breakdown

A typical week looks like this:

For placement, there may be additional hours.

Learning terms

On-campus: This includes tutor-led sessions such as seminars, lab sessions and demonstrations as well as student-led sessions for work in small groups.

Online learning: This is teaching that is delivered online using tools like Skype or Zoom, as well as work that you do yourself using online teaching resources.

Independent study: This is the work you do in your own time including reading and research.

Part-time study

You can also study this course part-time.

We have a strong support network online and on campus to help you develop your academic skills. We offer one-to-one and group sessions to develop your learning skills together with academic support from our library, IT teams and learning experts.

Throughout the year, we'll assess your technical skills through a series of assignments. Every week, we'll give you lab tasks designed to match the content covered in the lecture. We expect these tasks to be completed during the lab and you'll receive timely feedback assessment.

We'll assess your understanding through a variety of assessment methods including coursework projects, in-class activities, and a portfolio of data science tasks.

Feedback

You'll evaluate your work, skills and knowledge and identify areas for improvement. Sometimes you'll work in groups and assess each other's progress.

Each term, you'll get regular feedback on your learning.